How to map forest structure from aircraft, one tree at a time.

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Forest structure is crucial for understanding ecosystem processes and services.
• Airborne laser scanning (ALS) is a key technology for environmental mapping.
• High-density ALS data enables individual tree crown (ITC) analysis, moving beyond stand-level assessments.

Purpose of the Study:

• To develop and validate a simple, effective method for mapping ITCs using ALS data.
• To predict individual tree diameter at breast height (DBH) and aboveground biomass (AGB) from ALS-derived metrics.
• To assess the method's generalization ability and accuracy compared to area-based approaches.

Main Methods:

• Delineation of individual tree crowns (ITCs) from ALS data to extract height and crown diameter.
• Development of novel allometric models to predict DBH and AGB from ITC metrics.
• Validation of the methodology using independent datasets from temperate forests.

Main Results:

• Accurate predictions of individual DBH (R² = .85) and AGB (R² = .78).
• Successful prediction and mapping of tree size distributions, including the Gini coefficient of DBHs.
• Demonstrated superior generalization ability compared to standard area-based methods, especially for Gini coefficient prediction.

Conclusions:

• The developed ALS-based method provides a significant advancement for detailed forest ecology and dynamics analysis.
• The approach is easily applicable across diverse forested regions, enhancing traditional remote sensing tools.
• The method effectively maps key forest structure parameters, contributing to improved ecosystem understanding.